Children's Hospital of Philadelphia is using open source AI tools built on NVIDIA technology to generate precise 3D hear

Children's Hospital of Philadelphia is using open source AI tools to model children's hearts in seconds, with the goal of enabling safer, more precise care for kids with congenital heart disease. About one percent of all live births involve a congenital heart defect, and no two are alike. A child born with a hole between the lower chambers of their heart or a leaking valve in the single pumping chamber keeping them alive needs care that fits their exact anatomy. Historically, the devices surgeons use were almost never designed with that specific child in mind.

Dr. Matthew Jolley, a cardiologist and researcher at Children's Hospital of Philadelphia, explained the challenge: "You've got a one-of-a-kind kid and an off-the-shelf device. Our job is to find what fits — and modeling lets us do that before anyone goes into the cath lab or operating room."

CHOP's cardiac modeling service, built on MONAI, an open source medical imaging framework cofounded by NVIDIA, takes images a child's care team already has, such as CT scans, MRI and three-dimensional ultrasound, and produces anatomically precise heart models in just seconds. A workflow that once required four hours of work by a skilled researcher now completes fast enough for routine clinical use. The approach is spreading: more than twenty children's hospitals across the United States now run cardiac modeling programs. At Boston Children's Hospital, modeling supports more than half of all cardiac surgeries, roughly five hundred cases a year. CHOP expects to reach about two hundred modeled cases this year.

When Jolley joined CHOP in 2015, three-dimensional echocardiography was just coming online. There were tools for modeling adult valves, but almost nothing built for the complex, small anatomies he was treating. His lab worked with the open source community to build SlicerHeart, an extension of three-dimensional Slicer open source software for visualizing, segmenting and analyzing three-dimensional medical images, and began developing workflows to model pediatric hearts and valves from multiple imaging modalities. For years, producing a single model meant a skilled research assistant spending hours at a workstation. Machine learning changed that. Using MONAI Label and NVIDIA's Auto3DSeg implementation, Jolley's team trained segmentation networks on pairs of prior images and models. The output meets the same quality standard a trained human would produce in seconds rather than hours.

For complex ventricular septal defects, holes between the heart's lower chambers, CHOP now models routinely before surgery. One early case demonstrated the value: a child had already undergone two failed repair attempts, with surgeons unable to locate the defect with traditional methods. The three-dimensional model clarified the anatomy and the repair succeeded on the first try. For cases like these, cardiac modeling has moved from research to standard of care.

CHOP is working with NVIDIA and the open source community to build biomechanics-focused simulation frameworks using Newton, an open source physics engine built on the NVIDIA Warp Python framework that runs physics simulations on GPUs. These frameworks can help doctors understand tissue material properties that determine how a device will deploy in a given patient. With GPU acceleration, CHOP can reduce the time needed for cardiac device simulation from up to four hours, or a full overnight run for multiple configurations, to near real time. In practice, a clinician could compare how different devices fit a child's specific anatomy and get results fast enough to inform a same-day decision. CHOP has begun to implement features built on Warp and Newton for the closure devices used to seal holes in children's hearts and hopes to apply similar methods for simulations of transcatheter valves.

About two point four million people in the United States live with congenital heart disease. Historically, this has been a population too rare and too diverse to attract traditional device company investment at the scale families need. No single company has built the tools Jolley's team requires, and no single institution could build them alone. Open source is the workaround. SlicerHeart's tools are free to use and build on. Researchers at Stanford and Boston Children's contribute additional tools alongside CHOP. A national consortium of children's hospitals is now forming to build the next generation of shared modeling infrastructure, with open source as the connective tissue across institutions. As Jolley said, "It's too small a population to support traditional commercial development by normal economics. But it's such an important problem that between the research community and philanthropy, people are getting behind it. Open source defies traditional economics for small and heterogeneous populations by allowing collaboration and progress without barriers."